BeatBox

Hipke, Kyle; Toomim, Michael; Fiebrink, Rebecca; Fogarty, James

doi:10.1145/2598153.2598189

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…In most cases, a modest vocal percussion dataset is recorded for evaluation purposes, which could be oriented to general rhythmic vocal percussion [8] [21] [19] [7] or just beatboxing [11] [22]. Some of the mentioned work target user-specific vocal percussion analysis rather than general methods that could work for everyone, as vocal imitation styles seem to change significantly from person to person [9] [19]. Nevertheless, the idea of a universal model for vocal percussion classification may also be plausible, as some studies have suggested that humans, when given the task of imitating non-vocal environmental sounds, naturally give realism to these imitations by focusing on certain characteristic perceptual features of the original sound queries [14].…”

Section: Previous Workmentioning

confidence: 99%

A New Dataset for Amateur Vocal Percussion Analysis

Delgado

McDonald

et al. 2019

Proceedings of the 14th International Audio Mostly Conference: A Journey in Sound

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The imitation of percussive instruments via the human voice is a natural way for us to communicate rhythmic ideas and, for this reason, it attracts the interest of music makers. Specifically, the automatic mapping of these vocal imitations to their emulated instruments would allow creators to realistically prototype rhythms in a faster way. The contribution of this study is twofold. Firstly, a new Amateur Vocal Percussion (AVP) dataset is introduced to investigate how people with little or no experience in beatboxing approach the task of vocal percussion. The end-goal of this analysis is that of helping mapping algorithms to better generalise between subjects and achieve higher performances. The dataset comprises a total of 9780 utterances recorded by 28 participants with fully annotated onsets and labels (kick drum, snare drum, closed hi-hat and opened hi-hat). Lastly, we conducted baseline experiments on audio onset detection with the recorded dataset, comparing the performance of four state-of-the-art algorithms in a vocal percussion context.

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Section: Previous Workmentioning

confidence: 99%

A New Dataset for Amateur Vocal Percussion Analysis

Delgado

McDonald

et al. 2019

Proceedings of the 14th International Audio Mostly Conference: A Journey in Sound

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Human-in-the-loop machine learning: a state of the art

et al. 2022

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Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. Depending on who is in control of the learning process, we can identify: active learning, in which the system remains in control; interactive machine learning, in which there is a closer interaction between users and learning systems; and machine teaching, where human domain experts have control over the learning process. Aside from control, humans can also be involved in the learning process in other ways. In curriculum learning human domain experts try to impose some structure on the examples presented to improve the learning; in explainable AI the focus is on the ability of the model to explain to humans why a given solution was chosen. This collaboration between AI models and humans should not be limited only to the learning process; if we go further, we can see other terms that arise such as Usable and Useful AI. In this paper we review the state of the art of the techniques involved in the new forms of relationship between humans and ML algorithms. Our contribution is not merely listing the different approaches, but to provide definitions clarifying confusing, varied and sometimes contradictory terms; to elucidate and determine the boundaries between the different methods; and to correlate all the techniques searching for the connections and influences between them.

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A Review of User Interface Design for Interactive Machine Learning

Dudley

Kristensson

2018

ACM Trans. Interact. Intell. Syst.

237

137

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Interactive Machine Learning (IML) seeks to complement human perception and intelligence by tightly integrating these strengths with the computational power and speed of computers. The interactive process is designed to involve input from the user but does not require the background knowledge or experience that might be necessary to work with more traditional machine learning techniques. Under the IML process, non-experts can apply their domain knowledge and insight over otherwise unwieldy datasets to find patterns of interest or develop complex data-driven applications. This process is co-adaptive in nature and relies on careful management of the interaction between human and machine. User interface design is fundamental to the success of this approach, yet there is a lack of consolidated principles on how such an interface should be implemented. This article presents a detailed review and characterisation of Interactive Machine Learning from an interactive systems perspective. We propose and describe a structural and behavioural model of a generalised IML system and identify solution principles for building effective interfaces for IML. Where possible, these emergent solution principles are contextualised by reference to the broader human-computer interaction literature. Finally, we identify strands of user interface research key to unlocking more efficient and productive non-expert interactive machine learning applications.

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BeatBox

Cited by 5 publications

References 9 publications

A New Dataset for Amateur Vocal Percussion Analysis

A New Dataset for Amateur Vocal Percussion Analysis

Human-in-the-loop machine learning: a state of the art

A Review of User Interface Design for Interactive Machine Learning

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